Complex neurophysiological systems exhibit many types of dynamical behavior that indicate the presence of nonlinear mechanisms, including i) fractal (chaotic) fluctuations, which are characterized by l/f-like spectra, positive Lyapunov exponents and finite correlation dimension; ii) abrupt changes (bifurcations); and iii) sustained oscillations. Recently developed methods of nonlinear mathematics might be appropriate to identify and analyze such nonlinear dynamical behavior, but the suitability of those methods for the analysis of neurophysiological data needs to be demonstrated. We have found that beat-to-beat fluctuations in heart rate provide a readily accessible model system for investigating the variety of nonlinear dynamics in a neuroautonomic network, and the general aim of this proposal is to evaluate the applicability of the nonlinear methodology to that data. Fluctuations in cardiac interbeat interval, which are modulated by parasympathetic-sympathetic interactions, will be recorded for 24 hour time periods and will then be subjected to digital signal processing for spectral analysis, phase space mapping, and calculation of fractal dimension and other nonlinear metrics. Our aim is to determine whether these analytical methods demonstrate the presence of nonlinear neuroautonomic control processes. Specifically, we propose: 1. To test the hypothesis that heartrate regulation in healthy individuals is governed by fractal (chaotic) neuroautonomic control mechanisms. 2. To test the hypothesis that a variety of dysfunctions result in heartrate data that exhibit bifurcations, nonlinear oscillations and the loss of fractal variability. 3. To develop a nonlinear mathematical neurophysiological model of neuroautonomic heartrate control that accounts for the fractal variability of normal heartrate fluctuations and for the bifurcations and oscillatory behavior actually observed under certain pathologic conditions.